{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Загрузка библиотек"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from copy import deepcopy\n",
    "import numpy as np\n",
    "from catboost import CatBoostClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Предобработка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions_train=pd.read_csv('data/transactions_train.csv')\n",
    "train_target=pd.read_csv('data/train_target.csv')\n",
    "agg_features=transactions_train.groupby('client_id')['amount_rur'].agg(['sum','mean','std','min','max']).reset_index()\n",
    "counter_df_train=transactions_train.groupby(['client_id','small_group'])['amount_rur'].count()\n",
    "cat_counts_train=counter_df_train.reset_index().pivot(index='client_id', \\\n",
    "                                                      columns='small_group',values='amount_rur')\n",
    "cat_counts_train=cat_counts_train.fillna(0)\n",
    "cat_counts_train.columns=['small_group_'+str(i) for i in cat_counts_train.columns]\n",
    "train=pd.merge(train_target,agg_features,on='client_id')\n",
    "train=pd.merge(train,cat_counts_train.reset_index(),on='client_id')\n",
    "transactions_test=pd.read_csv('data/transactions_test.csv')\n",
    "test_id=pd.read_csv('data/test.csv')\n",
    "agg_features_test=transactions_test.groupby('client_id')['amount_rur'].agg(['sum','mean','std','min','max']).reset_index()\n",
    "counter_df_test=transactions_test.groupby(['client_id','small_group'])['amount_rur'].count()\n",
    "cat_counts_test=counter_df_test.reset_index().pivot(index='client_id', columns='small_group',values='amount_rur')\n",
    "cat_counts_test=cat_counts_test.fillna(0)\n",
    "cat_counts_test.columns=['small_group_'+str(i) for i in cat_counts_test.columns]\n",
    "test=pd.merge(test_id,agg_features_test,on='client_id')\n",
    "test=pd.merge(test,cat_counts_test.reset_index(),on='client_id')\n",
    "common_features=list(set(train.columns).intersection(set(test.columns)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train['bins']\n",
    "X = train[common_features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Нахождение маловажных колонок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "catboost_model = CatBoostClassifier()\n",
    "catboost_model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[14,\n",
       " 16,\n",
       " 17,\n",
       " 20,\n",
       " 31,\n",
       " 32,\n",
       " 36,\n",
       " 37,\n",
       " 40,\n",
       " 45,\n",
       " 53,\n",
       " 62,\n",
       " 76,\n",
       " 78,\n",
       " 79,\n",
       " 80,\n",
       " 85,\n",
       " 96,\n",
       " 109,\n",
       " 111,\n",
       " 118,\n",
       " 120,\n",
       " 122,\n",
       " 144,\n",
       " 146,\n",
       " 151,\n",
       " 152,\n",
       " 155,\n",
       " 166,\n",
       " 167,\n",
       " 170,\n",
       " 190,\n",
       " 192,\n",
       " 193,\n",
       " 201]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = catboost_model.feature_importances_\n",
    "not_needed = []\n",
    "for i in range(len(imp)):\n",
    "    if round(imp[i], 3) == 0:\n",
    "        not_needed.append(i)\n",
    "not_needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train['bins']\n",
    "X = train[common_features]\n",
    "X = X.drop('client_id', axis=1)\n",
    "X.drop(X.columns[not_needed], axis=1, inplace=True)\n",
    "X_exp = deepcopy(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_exp = (X - X.min())/(X.max() - X.min())\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_exp, y, test_size=0.2, random_state=62)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Создание Моделей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Использование Логистической Регрессии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6125"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model2 = LogisticRegression(max_iter=1000, n_jobs=-1, solver='saga', penalty='l1', C=568)\n",
    "model2.fit(X_train ** 0.22, y_train)\n",
    "model2.score(X_test ** 0.22, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обучение дерева"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5256666666666666"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {\n",
    "    'max_depth': range(1, 16)[::2],\n",
    "    'min_samples_leaf': range(1, 10),\n",
    "    'min_samples_split': range(2, 10)[::2]}\n",
    "clf_max = tree.DecisionTreeClassifier()\n",
    "grid_search_cv_clf = GridSearchCV(clf_max, parameters, cv=3, n_jobs=-1)\n",
    "grid_search_cv_clf.fit(X_train, y_train)\n",
    "grid_search_cv_clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обучение Леса потеряно в веках, но оно не увенчалось успехом."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обучение итоговой модели(на GPU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "catboost_model2 = CatBoostClassifier(iterations=3350, task_type=\"GPU\")\n",
    "catboost_model2.fit(X_train, y_train)\n",
    "results = catboost_model2.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6251666666666666"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Другие интересные идеи, которые мы попробовали"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ансамбль 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Идея состояла в том, чтобы сделать 3 модели. Первая различает между первыми двумя и вторыми двумя группами. Потом в зависимости от ответа спрашивает у нужной модели окончательный ответ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](img1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ансамбль 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сделать 3 модели разных архитектур, которые скажут свои ответы последней модели, которая уже выдвинет вердикт"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](img2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
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